Researchers at Charles Darwin University (CDU) in Australia's Northern Territory have developed FNS-Metrics, a solar forecasting system that uses seasonal information from First Nations calendars. The team fed this data into Conv-Ensemble, a new AI prediction model they designed.
The outcome of the study was a solar prediction error rate less than half that of current forecasting models, representing a 14.6% increase in accuracy and a 26.2% reduction in error compared to a strong baseline model.
Researchers developed the AI model using the Tiwi, Gulumoerrgin (Larrakia), Kunwinjku and Ngurrungurrudjba First Nations calendars, along with a modern calendar known as Red Centre. They said the system has the potential to revolutionize prediction technology.
The Conv-Ensemble model uses Conv1D layers to detect large-scale patterns in the data. It incorporates transformer and long short-term memory (LSTM) networks to refine detailed trends. These components are combined using a machine learning technique called weighted feature concatenation to generate the most accurate prediction.
To test the approach, the researchers drew solar power and weather data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs, with results showing the model can predict solar power generation with a lower error rate.
CDU Co-author, PhD student and Bundjalang man Luke Hamlin said the environmental knowledge held within the calendars is an invaluable resource.
“Incorporating First Nations seasonal knowledge into solar power generation predictions can significantly enhance accuracy by aligning forecasts with natural cycles that have been observed and understood for thousands of years,” said Hamlin, “Unlike conventional calendar systems, these seasonal insights are deeply rooted in local ecological cues, such as plant and animal behaviors, which are closely tied to changes in sunlight and weather patterns.”
Hamlin said that by integrating this knowledge, predictions can be tailored to reflect more granular shifts in environmental conditions, leading to more precise and culturally informed forecasting for specific regions across Australia.
Image: Charles Darwin University
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